Techniques for behavioral pairing in a contact center system

ABSTRACT

Techniques for behavioral pairing in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for pairing in a contact center including ordering one or more contacts, ordering one or more agents, comparing a first difference in ordering between a first contact and a first agent in a first pair with a second difference in ordering between a second contact and a second agent in a second pair, and selecting the first pair or the second pair for connection based on the comparing, wherein the first contact and the second contact are different or the first agent and the second agent are different.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/926,404, filed Jul. 10, 2020, which is a continuation of U.S. patentapplication Ser. No. 16/035,428, filed Jul. 13, 2018, now U.S. Pat. No.10,721,357, issued Jul. 21, 2020, which is a continuation of U.S. patentapplication Ser. No. 15/000,797, filed Jan. 19, 2016, now U.S. Pat. No.10,051,124, issued Aug. 14, 2018, which is a continuation of U.S. patentapplication Ser. No. 14/871,658, filed Sep. 30, 2015, now U.S. Pat. No.9,300,802, issued Mar. 29, 2016, which is a continuation-in-part of U.S.patent application Ser. No. 12/021,251, filed Jan. 28, 2008, now U.S.Pat. No. 9,712,679, issued Jul. 18, 2017, and is a continuation-in-partof U.S. patent application Ser. No. 14/530,058, filed Oct. 31, 2014, nowU.S. Pat. No. 9,277,055, issued Mar. 1, 2016, which is a continuation ofU.S. patent application Ser. No. 13/843,724, filed Mar. 15, 2013, nowU.S. Pat. No. 8,879,715, issued Nov. 4, 2014, which claims priority toU.S. Provisional Patent Application No. 61/615,788, filed Mar. 26, 2012,U.S. Provisional Patent Application No. 61/615,779, filed Mar. 26, 2012,and U.S. Provisional Patent Application No. 61/615,772, filed Mar. 26,2012, each of which is hereby incorporated by reference in its entiretyas if fully set forth herein.

FIELD OF THE DISCLOSURE

This disclosure generally relates to contact centers and, moreparticularly, to techniques for behavioral pairing in a contact centersystem.

BACKGROUND OF THE DISCLOSURE

A typical contact center algorithmically assigns contacts arriving atthe contact center to agents available to handle those contacts. Attimes, the contact center may have agents available and waiting forassignment to inbound or outbound contacts (e.g., telephone calls,Internet chat sessions, email). At other times, the contact center mayhave contacts waiting in one or more queues for an agent to becomeavailable for assignment.

In some typical contact centers, contacts are assigned to agents orderedbased on time of arrival, and agents receive contacts ordered based onthe time when those agents became available. This strategy may bereferred to as a “first-in, first-out”, “FIFO”, or “round-robin”strategy. In some contact centers, contacts or agents are assigned intodifferent “skill groups” or “queues” prior to applying a FIFO assignmentstrategy within each such skill group or queue. These “skill queues” mayalso incorporate strategies for prioritizing individual contacts oragents within a baseline FIFO ordering. For example, a high-prioritycontact may be given a queue position ahead of other contacts whoarrived at an earlier time, or a high-performing agent may be orderedahead of other agents who have been waiting longer for their next call.Regardless of such variations in forming one or more queues of callersor one or more orderings of available agents, contact centers typicallyapply FIFO to the queues or other orderings. Once such a FIFO strategyhas been established, assignment of contacts to agents is automatic,with the contact center assigning the first contact in the ordering tothe next available agent, or assigning the first agent in the orderingto the next arriving contact. In the contact center industry, theprocess of contact and agent distribution among skill queues,prioritization and ordering within skill queues, and subsequent FIFOassignment of contacts to agents is typically managed by a systemreferred to as an “Automatic Call Distributor” (“ACD”).

Some contact centers may use a “performance-based routing” or “PBR”approach to ordering the queue of available agents or, occasionally,contacts. For example, when a contact arrives at a contact center with aplurality of available agents, the ordering of agents available forassignment to that contact would be headed by the highest-performingavailable agent (e.g., the available agent with the highest salesconversion rate, the highest customer satisfaction scores, the shortestaverage handle time, the highest performing agent for the particularcontact profile, the highest customer retention rate, the lowestcustomer retention cost, the highest rate of first-call resolution). PBRordering strategies attempt to maximize the expected outcome of eachcontact-agent interaction but do so typically without regard forutilizing agents in a contact center uniformly. Consequently,higher-performing agents may receive noticeably more contacts and feeloverworked, while lower-performing agents may receive fewer contacts andidle longer, potentially reducing their opportunities for training andimprovement as well as potentially reducing their compensation.

In view of the foregoing, it may be understood that there is a need fora system that both attempts to balance the utilization of agents whileimproving contact center performance beyond what FIFO strategiesdeliver.

SUMMARY OF THE DISCLOSURE

Techniques for behavioral pairing in a contact center system aredisclosed. In one particular embodiment, the techniques may be realizedas a method for pairing in a contact center comprising ordering one ormore contacts, ordering one or more agents, comparing, by at least oneprocessor, a first difference in ordering between a first contact and afirst agent in a first pair with a second difference in ordering betweena second contact and a second agent in a second pair, and selecting, bythe at least one processor, the first pair or the second pair forconnection based on the comparing, wherein the first contact and thesecond contact may be different or the first agent and the second agentmay be different.

In accordance with other aspects of this particular embodiment,selecting the first pair or the second pair based on the comparing mayfurther comprise applying, by the at least one processor, a diagonalstrategy to the orderings.

In accordance with other aspects of this particular embodiment, theordering of one or more contacts or the ordering of one or more agentsmay be expressed as percentiles.

In accordance with other aspects of this particular embodiment, theordering of one or more contacts or the ordering of one or more agentsmay be expressed as percentile ranges.

In accordance with other aspects of this particular embodiment, each ofthe one or more contacts or each of the one or more agents may beassigned a percentile within each contact or agent's respectivepercentile range.

In accordance with other aspects of this particular embodiment, anassigned percentile may be a midpoint of a percentile range.

In accordance with other aspects of this particular embodiment, anassigned percentile may be a random percentile of a percentile range.

In accordance with other aspects of this particular embodiment, themethod may further comprise determining, by the at least one processor,a bandwidth for each contact type of the first and second contactsproportionate to a frequency at which contacts of each contact typebecome available for assignment.

In accordance with other aspects of this particular embodiment, themethod may further comprise targeting, by the at least one processor, abalanced agent utilization.

In accordance with other aspects of this particular embodiment,targeting the balanced agent utilization may further comprisedetermining, by the at least one processor, proportional bandwidth foreach of the one or more agents.

In accordance with other aspects of this particular embodiment, aselected agent of the selected pair may not be any of an agent laggingin a fairness metric, an agent rated highest in a performance metric, anagent rated highest in a performance metric for a particular contacttype, an agent previously assigned to a contact of the selected pair, asequentially labeled agent, or a randomly selected agent.

In accordance with other aspects of this particular embodiment, aselected contact of the selected pairing may not be any of a contact ata head of a queue in the contact center, a longest-waiting contact, ahighest-priority contact, or a randomly selected contact.

In accordance with other aspects of this particular embodiment, theselected one of the first pair and the second pair may comprise a worseexpected instant outcome than the other of the first pair and the secondpair.

In accordance with other aspects of this particular embodiment, ahigher-ordered agent may remain available for subsequent assignment to asimilarly higher-ordered contact, or a higher-ordered contact may remainavailable for subsequent assignment to a similarly higher-ordered agent.

In another particular embodiment, the techniques may be realized assystem for pairing in a contact center system comprising at least oneprocessor, wherein the at least one processor may be configured toperform the above-described method.

In another particular embodiment, the techniques may be realized as anarticle of manufacture for pairing in a contact center system comprisinga non-transitory processor readable medium and instructions stored onthe medium, wherein the instructions may be configured to be readablefrom the medium by at least one processor and thereby may cause the atleast one processor to operate so as to perform the above-describedmethod.

The present disclosure will now be described in more detail withreference to particular embodiments thereof as shown in the accompanyingdrawings. While the present disclosure is described below with referenceto particular embodiments, it should be understood that the presentdisclosure is not limited thereto. Those of ordinary skill in the arthaving access to the teachings herein will recognize additionalimplementations, modifications, and embodiments, as well as other fieldsof use, which are within the scope of the present disclosure asdescribed herein, and with respect to which the present disclosure maybe of significant utility.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to facilitate a fuller understanding of the present disclosure,reference is now made to the accompanying drawings, in which likeelements are referenced with like numerals. These drawings should not beconstrued as limiting the present disclosure, but are intended to beillustrative only.

FIG. 1 shows a schematic representation of a queue according toembodiments of the present disclosure.

FIG. 2 shows a schematic representation of a queue according toembodiments of the present disclosure.

FIG. 3 shows a schematic representation of a queue according toembodiments of the present disclosure.

FIG. 4 shows a schematic representation of a queue according toembodiments of the present disclosure.

FIG. 5 shows a schematic representation of a queue according toembodiments of the present disclosure.

FIG. 6 shows a flow diagram of a behavioral pairing method according toembodiments of the present disclosure.

FIG. 7 shows a block diagram of a contact center system according toembodiments of the present disclosure.

DETAILED DESCRIPTION

A typical contact center algorithmically assigns contacts arriving atthe contact center to agents available to handle those contacts. Attimes, the contact center may be in an “L1 state” and have agentsavailable and waiting for assignment to inbound or outbound contacts(e.g., telephone calls, Internet chat sessions, email). At other times,the contact center may be in an “L2 state” and have contacts waiting inone or more queues for an agent to become available for assignment. SuchL2 queues could be inbound, outbound, or virtual queues. Contact centersystems implement various strategies for assigning contacts to agents inboth L1 and L2 states.

The present disclosure generally relates to contact center systems,traditionally referred to as “Automated Call Distribution” (“ACD”)systems. Typically, such an ACD process is subsequent to an initial“Skills-based Routing” (“SBR”) process that serves to allocate contactsand agents among skill queues within the contact center. Such skillqueues may distinguish contacts and agents based on languagecapabilities, customer needs, or agent proficiency at a particular setof tasks.

The most common traditional assignment method within a queue is“First-In, First-Out” or “FIFO” assignment wherein the longest-waitingcontact is assigned to the longest-waiting agent. Some contact centersimplement “Performance-based Routing” (“PBR”) wherein the longestwaiting contact is assigned to the highest performing available agent.Variations of both such assignment methods commonly exist. For example,FIFO may select the least utilized available agent rather than thelongest waiting agent. More generally, FIFO may select an agent mostlagging in a particular metric or metrics. FIFO may also order queues ofcontacts where higher priority contact types may be positioned in aqueue ahead of lower priority contact types. Similarly, PBR may bemodified such that agent performance rankings may be altered dependingon the type of contact pending assignment (e.g., Bala et al., U.S. Pat.No. 7,798,876). PBR may also be modified to avoid an extreme unbalancingof agent utilization by setting limits on maximum or minimum agentutilization relative to peers.

Variations of FIFO typically target “fairness” inasmuch as they aredesigned to balance the allocation of contacts to agents over time. PBRadopts a different approach in which the allocation of contacts toagents is purposefully skewed to increase the utilization ofhigher-performing agents and reduce the utilization of lower-performingagents. PBR may do so despite potential negative impacts on morale andproductivity over time resulting from fatigue in over-utilized agentsand inadequate opportunity for training and compensation inunder-utilized agents.

Other eclectic assignment strategies are uncommonly, if ever, practiced.For instance, contacts may be randomly assigned to agents, irrespectiveof time of arrival, agent performance, or other variables.Alternatively, contact centers may seek to assign contacts to agentswith whom they have had a recent previous interaction. Additionally,agents in an L1 scenario may be selected sequentially based on a“labelling strategy” in which a recurrent algorithmic ordering of agentassignment is predefined (“Agent 1”, “Agent 2”, “Agent 3”, “Agent 1”,“Agent 2”, “Agent 3”, “Agent 1”, etc.).

In particular, the present disclosure refers to optimized strategies forassigning contacts to agents that improve upon traditional assignmentmethods. The present disclosure refers to such strategies as “BehavioralPairing” or “BP” strategies. Behavioral Pairing targets balancedutilization of agents within queues (e.g., skill queues) whilesimultaneously improving overall contact center performance potentiallybeyond what FIFO or PBR methods will achieve in practice. This is aremarkable achievement inasmuch as BP acts on the same contacts and sameagents as FIFO or PBR methods, approximately balancing the utilizationof agents as FIFO provides, while improving overall contact centerperformance beyond what either FIFO or PBR provide in practice.

BP improves performance by assigning agent and contact pairs in afashion that takes into consideration the assignment of potentialsubsequent agent and contact pairs such that when the benefits of allassignments are aggregated they may exceed those of FIFO and PBRstrategies. In some cases, BP results in instant contact and agentpairings that may be the reverse of what FIFO or PBR would indicate. Forexample, in an instant case BP might select the shortest-waiting contactor the lowest-performing available agent. BP respects “posterity”inasmuch as the system allocates contacts to agents in a fashion thatinherently forgoes what may be the highest-performing selection at theinstant moment if such a decision increases the probability of bettercontact center performance over time.

FIG. 1 illustrates a mechanism by which BP improves on FIFO and PBRstrategies. This example addresses a simplified hypothetical case (Queue100) in which two types of contacts may be assigned to either of twoagents in an environment in which the contact center is seeking tomaximize sales. The two evenly distributed contact types are a “60%Contact” and “20% Contact”, with the former more likely to make apurchase. The two agents are a “50% Agent” and a “30% Agent”, with theformer more likely to make a sale. This example further presumes thatthe four possible interactions between contacts and agents aremultiplicative in outcome such that when a 60% Contact is assigned the50% Agent, the overall probability of a sale is 30%. Accordingly, thefour possible outcomes in this example are 6%, 10%, 18%, and 30%.

In FIFO Strategy 110, all four possible outcomes are equally likely. Forexample, if a 60% Contact arrives with both the 30% Agent and the 50%Agent available, either agent might be selected with equal probabilitybased on, for example, which agent has been waiting longer or has beenutilized less. Similarly, if the 30% Agent comes available with both a60% Contact and a 20% Contact in Queue 100, either contact may beselected with equal probability and equal priority based on, forexample, which contact has been waiting longer (e.g., earlier time ofarrival). Therefore, in FIFO Strategy 100, the overall expected salesoutput of Queue 100 would be (6%+10%+18%+30%)/4=16%.

In PBR Strategy 120, the 50% Agent is preferentially assigned contactswhenever the 50% Agent is available. Therefore, PBR Strategy 120 wouldachieve its highest overall expected sales output in the case where the50% Agent is always available upon arrival of a contact. This peakexpectation is (10%+30%)/2=20%. However, this peak expectation isunlikely to be achieved in practice. For example, contacts may arrivewhile the 50% Agent is engaged and the 30% Agent is available. In thisinstance, PBR Strategy 120 would assign the contact to the 30% Agent.Thus, PBR performance in practice will approximate the performance ofFIFO Strategy 110 in proportion to the percentage of instances in whichnon-preferred assignments occur. In many cases, multiple contacts may bewaiting in Queue 100 (L2 state), and there may not be an opportunity topreferentially select the 50% Agent. If Queue 100 were persistently inan L2 state, PBR Strategy 120 would be expected to perform at the samerate as FIFO Strategy 110. In fact, if half the time Queue 100 were inan L2 state, and a further quarter of the time 50% Agent was unavailablebecause 50% Agent had been preferentially selected, then PBR Strategy120 would still offer no expected improvement over FIFO Strategy 110. InQueue 100, PBR Strategy 120 only offers significant performance benefitover FIFO Strategy 110 when Queue 100 is in an L1 state for an extendedperiod and, within that L1 state, there exists choice between the 50%Agent and the 30% Agent. However, in this case Queue 100 may be“overstaffed” inasmuch as it would require significant idle labor forpotentially minor benefit. Accordingly, in practice PBR may beineffective at substantially improving performance over FIFO.

In BP Strategy 130, a 20% Contact is preferentially assigned to the 30%Agent, and a 60% Contact is preferentially assigned to the 50% Agent.Therefore, the peak expectation of Queue 100 performance under BPStrategy 130 is (6%+30%)/2=18%. Importantly, this peak expectation doesnot erode like PBR Strategy 120 in an L2 state. Hypothetically, if therewas an arbitrarily long queue of contacts in a persistent L2 state, BPStrategy 130 would in fact operate at peak expected performance becausewhenever the 30% Agent became available there would be a 60% Contactpending assignment, and whenever the 50% Agent became available therewould be a 20% Contact pending assignment.

Even though there are only two agents maximally available in Queue 100,BP Strategy 130 may still outperform PBR Strategy 120 in an L1 state.For example, if the 50% Agent was occupied half of the time, PBRStrategy 120 would deliver no benefit as the other half of the time PBRStrategy 120 would be forced to select the 30% Agent. However, in an L1state under BP Strategy 130, availability of a 20% Contact would triggeruse of the lower-performing 30% Agent in the instant pairing, therebypreserving the higher-performing 50% Agent for subsequent assignment.Thereafter, in the next iteration, if a 60% Contact became available forassignment then the assignment of the preserved 50% Agent would resultin delivering BP Strategy 130's expected peak overall performance of18%. This should occur approximately half the time, resulting in asignificant improvement over both FIFO and PBR assignment strategies.When a pairing is to be made, the available agents may be ordered, andthe available contacts may be ordered. In an L1 state, in which only onecontact is available for assignment to an agent, the ordering of theagent is trivial. Similarly, in an L2 state, in which only one agent isavailable for assignment to a contact, the ordering of the contact istrivial.

In FIG. 1, the 50% Agent was selected for preferential pairing with 60%Contacts, and the 30% Agent was selected for preferential pairing with20% Contacts. A naïve interpretation of FIG. 1 might suggest that thisprocess may be as simple as assigning contact types to agents that areclosest in some metric (in FIG. 1, the probability of contributing to asuccessful sale). However, this may be an inefficient approach. FIG. 2further advances the concept of BP by illustrating how such a naïveapproach may be inefficient.

In FIG. 2, hypothetical Queue 200 serves two types of contacts: “40%Contacts” that contribute a 40% probability towards a purchase, and “20%Contacts”. Queue 200 also has two agents: an “80% Agent” thatcontributes an 80% probability towards a sale, and a “60% Agent”.Notably, both 40% Contacts and 20% Contacts are closer in their chosenmetric to the 60% Agent than to the 80% Agent. Accordingly, Naïve BPStrategy 230 would preferentially pair both 20% Contacts and 40%Contacts to the 60% Agent, leaving higher-performing 80% Agent idle.This may, in fact, result in an expected peak outcome of(12%+24%)/2=18%, significantly worse than either FIFO Strategy 210 withexpected outcome of (12%+16%+24%+32%)/4=21%, or PBR Strategy 220 withexpected peak outcome of (16%+32%)/2=24%.

FIG. 3 illustrates how the naïve approach of FIG. 2 may be improved.Queue 300 is similar to Queue 200 inasmuch as there are two types ofcontacts that occur with equal frequency, two agents, and fourcorresponding quadrants with sale probabilities identical to those ofQueue 200. However, contacts are no longer labeled based on theircontribution towards a probability of purchase. Instead, the contactshave been ordered based on their relative contribution and thenpercentiled such that the lower-ordered contacts occupy percentile rangeof 0% to 50%, with a midpoint of 25% (“0.25 Contacts”) and thehigher-ordered contacts occupy the percentile range of 50% to 100%, witha midpoint of 75% (“0.75 Contacts”). For clarity, this disclosure refersto percentile ranges as ranges of percentages, or fractional percentagesranging between 0 and 1. In some embodiments, other n-tile or percentageranges may be used.

Similarly, the agents have been ordered and percentiled into a 0.25Agent and a 0.75 Agent. Contacts are then positioned on a first axis (inthis instance, the Y-axis, or the rows of a grid) in order from lowestpercentile midpoint to highest, and agents are similarly positioned on asecond axis (in this instance, the X-axis, or the columns of the grid).Under such a structure, the diagonal strategy of assigning pairings ofcontacts with agents with the closest percentile midpoints is animproved mechanism of structuring a BP strategy, in this case BPStrategy 330. Under BP Strategy 330, the expected peak performance ofQueue 300 would be (12%+32%)/2=22%, which exceeds FIFO Strategy 310'sexpected performance of (12%+16%+24%+32%)/4=21%, and may potentiallyexceed PBR Strategy 320's expected peak performance of (16%+32%)/2=24%.

While FIG. 1 and FIG. 3 illustrate how BP can improve performance overFIFO and PBR strategies, they do so by making the assumption thatdifferent types of contacts arrive in equal proportion. Such anassumption may be incorrect in practice, and extending the concepts ofFIGS. 1 and 3 into a more common environment of more than two contacttypes or into a more common environment where the proportions of contacttypes vary may prove to be inefficient. Similarly, FIGS. 1 and 3 assumethat only two contact center agents are assigned to a queue. However, inmany contact centers the actual number of agents assigned to a queue issignificantly greater.

FIG. 4 illustrates how the strategy of FIG. 3 may prove to beinefficient and how to improve on such a strategy. Hypothetical Queue400 defines three contact types: “0.24 Contact” type that spans apercentile range of 0% to 48%, “0.65 Contact” type that spans apercentile range of 48% to 82%, and “0.91 Contact” type that spans apercentile range of 82% to 100%. Therefore, 0.24 Contacts constitute 48%of the contact center contacts, 0.65 Contacts constitute 34%, and 0.91Contacts represent the balance of 18%. Therefore, 0.24 Contacts may beassumed to be the lowest-ordered contacts along some metric, while 0.91Contacts may be assumed to be the highest-ordered.

Queue 400 has three equally available agents: a “0.166 Agent”, a “0.500Agent”, and a “0.833 Agent”. The 0.166 Agent occupies the midpoint ofpercentile range 0% to 33.3% and therefore is the lowest-ordered agentaccording to some metric, while correspondingly 0.833 Agent occupies themidpoint of percentile range 66.6% to 100% and is therefore thehighest-ordered agent. Agent 0.500 is the middle-ordered agent occupyingthe percentile range of 33.3% to 66.6%.

Inefficient BP Strategy 410 would strictly extend the strategy in FIG. 3of preferentially pairing contacts with agents based on proximity inpercentile midpoint. This would result in 0.24 Contacts beingpreferentially assigned to 0.166 Agent, 0.65 Contacts being assigned to0.500 Agent, and 0.91 Contacts being assigned to 0.833 Agent. Such astrategy would create a potential inefficiency inasmuch as it would tendtowards utilizing Agent 0.166 most heavily and Agent 0.833 leastheavily. This is so because the 0.24 Contacts represent 48% of allcontacts and are preferentially allocated to Agent 0.166 that onlyrepresents 33.3% of agent availability. Similarly, the 0.91 Contactsrepresent 18% of all contacts and are preferentially allocated to Agent0.833, also representing 33.3% of agent availability and hencepotentially underutilized as a result. Such a bias towards utilizinglower-ordered Agents may result in Inefficient BP Strategy 410delivering a suboptimal performance which may be below that of FIFO orPBR.

FIG. 5 illustrates techniques to improve on Inefficient BP Strategy 410according to some embodiments of the present disclosure. Queue 500 issubstantially similar to Queue 400 inasmuch as there are also threecontact types occupying percentile ranges 0% to 48%, 48% to 82%, and 82%to 100%. Queue 500 also has three agents occupying percentile ranges of0% to 33.3%, 33.3% to 66.6%, and 66.6% to 100%, correspondingly named“0.166 Agent”, “0.500 Agent”, and “0.833 Agent” to indicate theirpercentile range midpoints. Unlike Queue 400 (FIG. 4), each contact typeis referred to by its percentile range rather than the midpoint of itsrange.

Efficient BP Strategy 510 improves on Inefficient BP Strategy 410 byseeking to most closely approximate a diagonal strategy. However, unlikethe simplified case of Queue 300 which by example was able to preciselyalign contact and agent percentiles, Efficient BP Strategy 510 returnsto targeting a balanced utilization of agents by relaxing the assumptionof a one-to-one correspondence between contact types and agents andinstead establishing a correspondence between ranges of percentiles.

In some embodiments, each contact may be assigned a percentile withinthe percentile range of each contact's type. These percentiles may beassigned randomly. In this scenario, some of the lowest-ordered,highest-frequency contacts (0% to 48% Contacts) may be preferablyassigned to the lowest-ordered 0.166 Agent, while others of thelowest-ordered, highest-frequency contacts may be preferably assigned tothe middle-ordered 0.500 Agent. Similarly, some of the middle-ordered,middle-frequency contacts (48% to 82% Contacts) may be preferablyassigned to the middle-ordered 0.500 Agent, while others may bepreferably assigned to the highest-performing 0.833 Agent.

For example, if a 0% to 48% Contact arrives, it may receive a randompercentile of 10% (0.10). Assuming all of the agents in Queue 500 areavailable for assignment, the diagonal strategy would preferably assignthis 0% to 48% Contact to the 0.166 Agent. Conceptually, a contactassigned a percentile of 10% falls within the percentile range (or“bandwidth”) accorded to the 0.166 Agent occupying the percentile rangeof 0% to 33.3%.

The next contact to arrive may be another a 0% to 48% Contact. In thisinstance, the contact may receive a random percentile of 42% (0.42).Again, assuming all of the agents in queue 500 are available forassignment, the diagonal strategy would preferably assign this 0% to 48%Contact to the 0.500 Agent, which occupies the percentile range of 33.3%to 66.6%. Under Efficient BP Strategy 510, each of the three agents isexpected to receive approximately one-third of all contacts over time,so the lowest-ordered 0.166 Agent is no longer over-utilized relative tothe other agents, and the highest-ordered 0.833 Agent is no longerunder-utilized relative to the other agents, as under Inefficient BPStrategy 410 (FIG. 4). Moreover, in some cases, expected peakperformance may be higher as in the case of Efficient BP Strategy 510compared with that of Inefficient BP Strategy 410 because higher-ordered(e.g., higher-performing) agents are utilized more under Efficient BPStrategy 510 than under Inefficient BP Strategy 410.

FIG. 6 depicts a behavioral pairing method 600 according to embodimentsof the present disclosure. At block 610, behavioral paring method 400may begin.

At block 610, a percentile (or n-tile, quantile, percentile range,bandwidth, or other type of “score” or range of scores, etc.) may bedetermined for each available contact. For situations in which contactsare waiting on hold in a queue, percentiles may be determined for eachof the contacts waiting on hold in the queue. For situations in whichcontacts are not waiting on hold in a queue, a percentile may beassigned to the next contact to arrive at the contact center. Thepercentiles may be bounded by a range of percentiles defined for aparticular type or group of contacts based on information about thecontact. The percentile bounds or ranges may be based on a frequencydistribution or other metric for the contact types. The percentile maybe randomly assigned within the type's percentile range.

In some embodiments, percentiles may be ordered according to aparticular metric or combination of metrics to be optimized in thecontact center, and a contact determined to have a relatively highpercentile may be considered to be a “higher-value” contact for thecontact center inasmuch as these contacts are more likely to contributeto a higher overall performance in the contact center. For example, arelatively high-percentile contact may have a relatively high likelihoodof making a purchase.

In some embodiments, a percentile may be determined for a contact at thetime the contact arrives at the contact center. In other embodiments, apercentile may be determined for the contact at a later point in time,such as when the contact arrives at a particular skill queue or ACDsystem, or when a request for a pairing is made.

After a percentile has been determined for each contact available forpairing, behavioral pairing method 600 may proceed to block 620. In someembodiments, block 620 may be performed prior to, or simultaneouslywith, block 610.

At block 620, a percentile may be determined for each available agent.For situations in which agents are idle, waiting for contacts to arrive,percentiles may be determined for each of the idle agents. Forsituations in which agents for a queue are all busy, a percentile may bedetermined to the next agent to become available. The percentiles may bebounded by a range of percentiles (e.g., “bandwidth”) defined based onall of the agents assigned to a queue (e.g., a skill queue) or only theavailable agents assigned to a particular queue. In some embodiments,the bounds or ranges of percentiles may be based on a desired agentutilization (e.g., for fairness, efficiency, or performance).

In some embodiments, agent percentiles may be ordered according to aparticular metric or combination of metrics to be optimized in thecontact center, and an agent determined to have a relatively highpercentile may be considered to be a higher-performing agent for thecontact center. For example, a relatively high-percentile agent may havea relatively high likelihood of making a sale.

In some embodiments, an agent's percentile may be determined at the timethe agent becomes available within the contact center. In otherembodiments, a percentile may be determined at a later point in time,such as when a request for a pairing is made.

After a percentile has been determined for each available agent andcontact, behavioral pairing method 600 may proceed to block 630.

At block 630, a pair of an available contact and an available agent maybe determined based on the percentiles determined for each availablecontact at block 610 and for each available agent at block 620. In someembodiments, the pair may be determined according to a diagonalstrategy, in which contacts and agents with more similar percentiles (orthe most similar percentiles) may be selected for pairing. For example,a behavioral pairing module may select a contact-agent pairing with thesmallest absolute difference between the contact's score and the agent'sscore.

In some situations, multiple agents may be idle when a contact arrives(an L1 state). Under BP, the newly available contact may be paired witha selected one of the available agents that has a score more similar tothe contact's score than other available agents. In other situations,multiple contacts may be waiting in a queue when an agent becomesavailable (an L2 state). Under BP, the newly available agent may bepaired with a selected one of the contacts waiting in the queue that hasa percentile more similar to the agent's percentile than other contactswaiting in the queue.

In some situations, selecting a pairing based on similarity of scoresmay result in selecting an instant pairing that might not be the highestperforming instant pairing, but rather increases the likelihood ofbetter future pairings.

After a pairing has been determined at block 630, behavioral pairingmethod 600 may proceed to block 640. At block 640, modules within thecontact center system may cause the contact and agent of thecontact-agent pair to be connected with one another. For example, abehavioral pairing module may indicate that an ACD system or otherrouting device may distribute a particular contact to a particularagent.

After connecting the contact and agent at block 640, behavioral pairingmethod 600 may end. In some embodiments, behavioral pairing method 600may return to block 630 for determining one or more additional pairings(not shown). In other embodiments, behavioral pairing method 600 mayreturn to block 610 or block 620 to determine (or re-determine)percentiles for available contacts or agents (not shown).

FIG. 5 shows a block diagram of a contact center system 700 according toembodiments of the present disclosure. The description herein describesnetwork elements, computers, and/or components of a system and methodfor simulating contact center systems that may include one or moremodules. As used herein, the term “module” may be understood to refer tocomputing software, firmware, hardware, and/or various combinationsthereof. Modules, however, are not to be interpreted as software whichis not implemented on hardware, firmware, or recorded on a processorreadable recordable storage medium (i.e., modules are not software perse). It is noted that the modules are exemplary. The modules may becombined, integrated, separated, and/or duplicated to support variousapplications. Also, a function described herein as being performed at aparticular module may be performed at one or more other modules and/orby one or more other devices instead of or in addition to the functionperformed at the particular module. Further, the modules may beimplemented across multiple devices and/or other components local orremote to one another. Additionally, the modules may be moved from onedevice and added to another device, and/or may be included in bothdevices.

As shown in FIG. 7, the contact center system may include a centralswitch 710. The central switch 710 may receive incoming contacts (e.g.,callers) or support outbound connections to contacts via a dialer, atelecommunications network, or other modules (not shown). The centralswitch 710 may include contact routing hardware and software for helpingto route contacts among one or more contact centers, or to one or morePBX/ACDs or other queuing or switching components within a contactcenter.

The central switch 710 may not be necessary if there is only one contactcenter, or if there is only one PBX/ACD routing component, in thecontact center system 700. If more than one contact center is part ofthe contact center system 700, each contact center may include at leastone contact center switch (e.g., contact center switches 720A and 720B).The contact center switches 720A and 720B may be communicatively coupledto the central switch 710.

Each contact center switch for each contact center may becommunicatively coupled to a plurality (or “pool”) of agents. Eachcontact center switch may support a certain number of agents (or“seats”) to be logged in at one time. At any given time, a logged-inagent may be available and waiting to be connected to a contact, or thelogged-in agent may be unavailable for any of a number of reasons, suchas being connected to another contact, performing certain post-callfunctions such as logging information about the call, or taking a break.

In the example of FIG. 5, the central switch 710 routes contacts to oneof two contact centers via contact center switch 720A and contact centerswitch 720B, respectively. Each of the contact center switches 720A and720B are shown with two agents each. Agents 730A and 730B may be loggedinto contact center switch 720A, and agents 730C and 730D may be loggedinto contact center switch 720B.

The contact center system 700 may also be communicatively coupled to anintegrated service from, for example, a third party vendor. In theexample of FIG. 5, behavioral pairing module 600 may be communicativelycoupled to one or more switches in the switch system of the contactcenter system 700, such as central switch 710, contact center switch720A, or contact center switch 720B. In some embodiments, switches ofthe contact center system 700 may be communicatively coupled to multiplebehavioral pairing modules. In some embodiments, behavioral pairingmodule 740 may be embedded within a component of a contact center system(e.g., embedded in or otherwise integrated with a switch).

Behavioral pairing module 740 may receive information from a switch(e.g., contact center switch 720A) about agents logged into the switch(e.g., agents 730A and 730B) and about incoming contacts via anotherswitch (e.g., central switch 710) or, in some embodiments, from anetwork (e.g., the Internet or a telecommunications network) (notshown).

The behavioral pairing module 740 may process this information and todetermine which contacts should be paired (e.g., matched, assigned,distributed, routed) with which agents. For example, multiple agents areavailable and waiting for connection to a contact (L1 state), and acontact arrives at the contact center via a network or central switch.As explained above, without the behavioral pairing module 740, a contactcenter switch will typically automatically distribute the new contact towhichever available agent has been waiting the longest amount of timefor an agent under a “fair” FIFO strategy, or whichever available agenthas been determined to be the highest-performing agent under a PBRstrategy.

With a behavioral pairing module 740, contacts and agents may be givenscores (e.g., percentiles or percentile ranges/bandwidths) according toa pairing model or other artificial intelligence data model, so that acontact may be matched, paired, or otherwise connected to a preferredagent.

In an L2 state, multiple contacts are available and waiting forconnection to an agent, and an agent becomes available. These contactsmay be queued in a contact center switch such as a PBX or ACD device(“PBX/ACD”). Without the behavioral pairing module 740, a contact centerswitch will typically connect the newly available agent to whichevercontact has been waiting on hold in the queue for the longest amount oftime as in a “fair” FIFO strategy or a PBR strategy when agent choice isnot available. In some contact centers, priority queuing may also beincorporated, as previously explained.

With a behavioral pairing module 740 in an L2 scenario, as in the L1state described above, contacts and agents may be given percentiles (orpercentile ranges/bandwidths, etc.) according to, for example, a model,such as an artificial intelligence model, so that an agent comingavailable may be matched, paired, or otherwise connected to a preferredcontact.

At this point it should be noted that behavioral pairing in a contactcenter system in accordance with the present disclosure as describedabove may involve the processing of input data and the generation ofoutput data to some extent. This input data processing and output datageneration may be implemented in hardware or software. For example,specific electronic components may be employed in a behavioral pairingmodule or similar or related circuitry for implementing the functionsassociated with behavioral pairing in a contact center system inaccordance with the present disclosure as described above.Alternatively, one or more processors operating in accordance withinstructions may implement the functions associated with behavioralpairing in a contact center system in accordance with the presentdisclosure as described above. If such is the case, it is within thescope of the present disclosure that such instructions may be stored onone or more non-transitory processor readable storage media (e.g., amagnetic disk or other storage medium), or transmitted to one or moreprocessors via one or more signals embodied in one or more carrierwaves.

The present disclosure is not to be limited in scope by the specificembodiments described herein. Indeed, other various embodiments of andmodifications to the present disclosure, in addition to those describedherein, will be apparent to those of ordinary skill in the art from theforegoing description and accompanying drawings. Thus, such otherembodiments and modifications are intended to fall within the scope ofthe present disclosure. Further, although the present disclosure hasbeen described herein in the context of at least one particularimplementation in at least one particular environment for at least oneparticular purpose, those of ordinary skill in the art will recognizethat its usefulness is not limited thereto and that the presentdisclosure may be beneficially implemented in any number of environmentsfor any number of purposes. Accordingly, the claims set forth belowshould be construed in view of the full breadth and spirit of thepresent disclosure as described herein.

1. A method comprising: receiving, by at least one computer processorcommunicatively coupled to and configured to operate in a contact centersystem, a pairing strategy for pairing a first available agent and asecond available agent to contacts associated with a contact centerskill; wherein the first available agent has a higher expectedperformance on a metric than the second available agent for a firstcontact, wherein the first available agent has a higher expectedperformance on the metric than the second available agent for a secondcontact, wherein the first agent became available before the secondagent, wherein the first and second available agents have a samepriority or no priority, wherein either ordering of pairing the firstand second available agents is possible even if the first and secondavailable agents have not previously interacted with the first or secondcontacts, wherein the pairing strategy is configured to improveaggregate performance on the metric for the first and second availableagents over a first-in first-out (FIFO) strategy or a random strategy.2. The method of claim 1, wherein the first contact is of a firstcontact type, and the second contact is of a second contact typedifferent from the first contact type.
 3. The method of claim 1, whereinthe metric is at least one of: a sales conversion rate, a customersatisfaction score, and an average handle time.
 4. The method of claim1, wherein the pairing strategy compares a percentile of the firstcontact or the second contact with percentiles of at least the firstagent and the second agent.
 5. The method of claim 1, whereinpercentiles of the first agent, the second agent, the first contact, andthe second contact are based on the metric.
 6. The method of claim 1,wherein the pairing strategy is a behavioral pairing strategy.
 7. Themethod of claim 1, wherein the pairing strategy is a diagonal pairingstrategy.
 8. A system comprising: at least one computer processorcommunicatively coupled to and configured to operate in a contact centersystem, wherein the at least one computer processor is furtherconfigured to: receive a pairing strategy for pairing a first availableagent and a second available agent to contacts associated with a contactcenter skill; wherein the first available agent has a higher expectedperformance on a metric than the second available agent for a firstcontact, wherein the first available agent has a higher expectedperformance on the metric than the second available agent for a secondcontact, wherein the first agent became available before the secondagent, wherein the first and second available agents have a samepriority or no priority, wherein either ordering of pairing the firstand second available agents is possible even if the first and secondavailable agents have not previously interacted with the first or secondcontacts, wherein the pairing strategy is configured to improveaggregate performance on the metric for the first and second availableagents over a first-in first-out (FIFO) strategy or a random strategy.9. The system of claim 8, wherein the first contact is of a firstcontact type, and the second contact is of a second contact typedifferent from the first contact type.
 10. The system of claim 8,wherein the metric is at least one of: a sales conversion rate, acustomer satisfaction score, and an average handle time.
 11. The systemof claim 8, wherein the pairing strategy compares a percentile of thefirst contact or the second contact with percentiles of at least thefirst agent and the second agent.
 12. The system of claim 8, whereinpercentiles of the first agent, the second agent, the first contact, andthe second contact are based on the metric.
 13. The system of claim 8,wherein the pairing strategy is a behavioral pairing strategy.
 14. Thesystem of claim 8, wherein the pairing strategy is a diagonal pairingstrategy.
 15. An article of manufacture comprising: a non-transitorycomputer processor readable medium; and instructions stored on themedium; wherein the instructions are configured to be readable from themedium by at least one computer processor communicatively coupled to andconfigured to operate in a contact center system and thereby cause theat least one computer processor to operate so as to: receive a pairingstrategy for pairing a first available agent and a second availableagent to contacts associated with a contact center skill; wherein thefirst available agent has a higher expected performance on a metric thanthe second available agent for a first contact, wherein the firstavailable agent has a higher expected performance on the metric than thesecond available agent for a second contact, wherein the first agentbecame available before the second agent, wherein the first and secondavailable agents have a same priority or no priority, wherein eitherordering of pairing the first and second available agents is possibleeven if the first and second available agents have not previouslyinteracted with the first or second contacts, wherein the pairingstrategy is configured to improve aggregate performance on the metricfor the first and second available agents over a first-in first-out(FIFO) strategy or a random strategy.
 16. The article of manufacture ofclaim 15, wherein the first contact is of a first contact type, and thesecond contact is of a second contact type different from the firstcontact type.
 17. The article of manufacture of claim 15, wherein themetric is at least one of: a sales conversion rate, a customersatisfaction score, and an average handle time.
 18. The article ofmanufacture of claim 15, wherein the pairing strategy compares apercentile of the first contact or the second contact with percentilesof at least the first agent and the second agent.
 19. The article ofmanufacture of claim 15, wherein percentiles of the first agent, thesecond agent, the first contact, and the second contact are based on themetric.
 20. The article of manufacture of claim 15, wherein the pairingstrategy is a behavioral pairing strategy.